performance testing - KNN Accuracy of 100%? -


i have used following code knn 

jd <- jobdata    head (jd)    jd$ipermanency rate= as.integer(as.factor(jd$ipermanency rate))    jd$`permanency rate`=as.integer(as.factor(jd$`permanency rate`))    jd$`job skills`=as.integer(as.factor(jd$`job skills`))    jd$default <- factor(jd$default)    num.vars <- sapply(jd, is.numeric)    jd[num.vars] <- lapply(jd[num.vars], scale)    jd$`permanency rate` <- factor(jd$`permanency rate`)    num.vars <- sapply(jd, is.numeric)    jd[num.vars] <- lapply(jd[num.vars], scale)    myvars <- c("permanency rate", "job skills")    jd.subset <- jd[myvars]    summary(jd.subset)    set.seed(123)    test <- 1:100    train.jd <- jd.subset[-test,]    test.jd <- jd.subset[test,]    train.def <- jd$`permanency rate`[-test]    test.def <- jd$`permanency rate`[test]    library(class)    knn.1 <-  knn(train.jd, test.jd, train.def, k=1)    knn.3 <-  knn(train.jd, test.jd, train.def, k=3)    knn.5 <- knn(train.jd, test.jd, train.def, k=5)

but whenever calculate proportion of correct classification k = 1, 3 & 5 100% correctness. normal or have gone wrong somewhere

thanks


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